Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse

Area of Science:

• Agricultural Engineering
• Wireless Communication
• Agroindustry Technology

Background:

• Greenhouse tomato production is economically vital in Spain, necessitating technological advancements like precision agriculture.
• Precision agriculture relies on wireless sensors, but signal propagation is hindered by vegetation, limiting network coverage.
• Existing radio wave propagation models (FSPL, ITU-R, etc.) show over 30% error in the 2.4 GHz band within greenhouse environments.

Purpose of the Study:

• To develop and validate optimized radio wave propagation models for wireless sensor networks in greenhouse environments.
• To address the limitations of current models in predicting signal behavior amidst dense vegetation.
• To enhance the reliability of precision agriculture systems in tomato production.

Main Methods:

• Field tests were conducted in greenhouse tomato cultivation settings.
• Performance evaluation of established propagation models (FSPL, ITU-R, etc.) at 2.4 GHz.
• Development and optimization of new propagation models tailored to greenhouse conditions.
• Comparison of optimized models against measured field data.

Main Results:

• Established propagation models exhibited prediction errors exceeding 30% in field tests.
• The newly developed optimized models demonstrated a significant reduction in error, with estimates below 9% in worst-case scenarios.
• Improved accuracy in predicting radio wave propagation within the greenhouse environment.

Conclusions:

• Optimized propagation models offer a substantial improvement over existing models for wireless sensor deployment in greenhouse agriculture.
• Enhanced model accuracy supports more reliable precision agriculture, boosting efficiency and yield in tomato production.
• These advancements benefit farmers, consumers, and the overall economic chain of greenhouse tomato cultivation.